The output of sensors with a high dynamic range can be difficult to render on typical imaging displays. The need to render large differences in intensity and the need to achieve sufficient contrast in areas of relatively uniform intensity compete with each other, given the limited dynamic range of available display devices. An example is a thermal infrared image of a warm airport runway that includes areas of clear sky. The runway will be hot relative to non-runway ground areas, and very hot relative to the sky areas. In this case, reliably representing the relative thermal differences among these three areas along with minute thermal differences within each of these areas would be impossible without use of some sort of non-linear, spatially sensitive intensity transform.
In addition to dynamic range limitations, typical display devices have characteristic intensity curves that result in differences in perceived intensity with varying input intensity. A particular intensity difference may, for example, be more perceptible if the average intensity is in the middle, as opposed to the extreme low end or high end, of the output range of the display.
The problem to be solved is the stabilizing of global intensity levels of the displayed image while optimizing local area detail. There exist a number of approaches to solving the problem, many of them under the category of histogram equalization (HE) (or “histogram stretching”) reviewed in Pier, Stephen M.; Amburn, E. Philip; Cromartie, Robert; et al., Adaptive histogram equalization and its variations, Computer Vision, Graphics, and Image Processing, vol. 39, issue 3, pp. 355-68, September 1987, and in Reza, Ali M., Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image Enhancement, Journal of VLSI Signal Processing Systems, vol. 38, no. 1, pp. 35-44, August 2004. In this technique, the brightness values of image pixels are reassigned based on a histogram of their input intensities. In the simplest “flattening” approach, the broad goal is to assign an equal number of pixels to each possible brightness level. In more sophisticated processors, the process is “adaptive” in that the nonlinear transformations are applied on a local area or multi-scale basis. Other operations such as minimum, maximum, and median filters, as well as clipping, may be applied.
Another approach is gradient domain dynamic range compression (GDDRC), which is described in Fattal, Raanan; Lischinski, Dani; Werman, Michael, Gradient domain high dynamic range compression, ACM Transactions on Graphics (TOG), vol. 21 no. 3, July 2002. The GDDRC technique works in the logarithmic domain to shrink large intensity gradients more aggressively than small gradients. This serves to reduce the global contrast ratio, while preserving local detail.
Histogram manipulations are effective for the particular problem of fine details in dark image regions. However, good quality image details can actually be degraded by naïve implementation of these algorithms. Neither HE nor GDDRC constitutes a seamless, esthetically pleasing and information-preserving solution to widely varying levels and contrasts over arbitrary image scenes.